计算机技术与发展2018，Vol.28Issue(1)：23-27,5.DOI:10.3969/j.issn.1673-629X.2018.01.005
权重随机正交化的极速非线性判别分析网络
Nonlinear Discriminant Analysis Networks with Random and Orthogonalized Input Weights
摘要
Abstract
Extreme Learning Machines ( ELM) has attracted increasing attention due to its fast training speed,simplicity and good gener-alization. However,in applications with high-dimensional features,a large amount of redundant information may exist in the data,which has not been concerned in classical ELM. Moreover,the discriminant information behind data has also not been incorporated in the ELM learning. To overcome the drawbacks of classical ELM,a weight-orthogonal discriminant analysis network ( O-ENDA) is put forward. In O-ENDA,the input weights of ELM are restricted to be orthogonal,removing the redundant features to alleviate the risk of over-fit-ting ( especially in the scenario of small samples);simultaneously the transformed hidden features are combined with discriminant analysis to improve the discriminant ability of O-ENDA. Experiment demonstrates that the proposed approach not only can remove redundant in-put information while preserving the necessary feature information,but also entirely yields preferable classification accuracy than classical ELM.关键词
极速学习机/线性判别分析/神经网络/降维/分类Key words
extreme learning machine/linear discriminant analysis/neural networks/dimension reduction/classification分类
信息技术与安全科学引用本文复制引用
谢群辉,田青..权重随机正交化的极速非线性判别分析网络[J].计算机技术与发展,2018,28(1):23-27,5.基金项目
国家自然科学基金资助项目(61472186) （61472186）
中国博士后科学基金特别资助项目(20133218110032) （20133218110032）
南京信息工程大学人才启动基金 （）
